Cyber-Physical Digital Twin for Asset Risk Forecasting
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Solution Overview
Problem
Current systems face challenges in accurately assessing and predicting the evolving operation or status of complex physical systems and assets, particularly in IoT environments, due to the complexity of monitoring numerous parameters and the difficulty in capturing temporal changes and risk events, which limits their ability to make proactive decisions and optimize maintenance.
Innovation Solution
A digital platform for avatar measurements that creates a digital twin of physical assets, allowing for real-time data integration and simulation-based forecasting of future states, enabling proactive decision-making and risk management by analyzing structural, operational, and environmental parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional monitoring systems are used to track physical assets, then basic data collection is possible, but accurate risk assessment and prediction of future states cannot be achieved
Solution Approach 1:
The patent creates a digital twin (virtual copy) of the physical asset that mirrors its structure, behavior, and parameters. This digital replica enables accurate risk assessment and prediction by simulating future states without requiring complex physical monitoring infrastructure, thus improving measurement precision while managing system complexity through virtualization
Solution Approach 2:
The system performs preliminary simulations and predictions by propagating digital twin parameters through time to forecast future asset states and potential risks. This allows proactive risk assessment and maintenance planning before actual failures occur, improving prediction accuracy by analyzing potential future scenarios
2Reliability
If comprehensive parameters are monitored in real-time, then accurate asset status is captured, but the complexity of data processing and analysis increases significantly
Solution Approach 1:
Instead of processing complex real-time data from multiple sensors directly, the patent creates a simplified digital twin that replicates essential asset parameters and behavior. This virtual model processes data more efficiently while maintaining reliable monitoring, reducing computational complexity while preserving monitoring reliability
Solution Approach 2:
The patent transitions from analyzing raw sensor data in the physical domain to analyzing propagated parameters in the digital/virtual domain. By moving the analysis to this another dimension (digital twin space), the system simplifies data processing while maintaining comprehensive monitoring capability
3Loss of time
If digital twin parameters are propagated through time to predict future states, then proactive risk management is enabled, but computational resources and time are consumed
Solution Approach 1:
The system performs preliminary time propagation simulations of the digital twin to predict future asset states and identify potential risks before they materialize. This enables proactive maintenance and risk mitigation, reducing downtime and loss by addressing issues before they affect physical operations, while allowing computational resources to be used during off-peak periods
Data Source
AI summary
A digital device is provided with circuitry configured to select object elements stored in a storage of the digital device, and assemble the selected object elements, a plurality of sensors configured to measure at least one parameter of structural parameter, operational parameter, and environmental status parameter, the at least one parameter being associated with a physical object represented by the assembled object elements, wherein the assembled object elements include data structure representing states of subsystems of the physical object, and the data structure holds a value of the at least one parameter.